Evaluation of the Complexity of Automated Trace Alignment using Novel Power Obfuscation Methods
Bozhi Liu, Kemeng Chen, Minjun Seo, Janet Wang, Roman Lysecky · 2018
This paper presents a methodology for evaluating power obfuscation approaches that seek to obfuscate the location of sensitive operations in the power trace, thereby increasing the complexity of automated trace alignment. The paper presents a new adversary model and proposes a new metric, mean trials to success (MTTS), to evaluate power obfuscation methods in the context of automated trace alignment. We evaluate two common obfuscation methods, namely instruction shuffling and random instruction insertion, and we present a new obfuscation method using power shaping to intentionally mislead the attacker.